{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(r\"C:\\Download\\FE_pima-indians-diabetes.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据探索分析部分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null float64\n",
      "Plasma_glucose_concentration    768 non-null float64\n",
      "blood_pressure                  768 non-null float64\n",
      "Triceps_skin_fold_thickness     768 non-null float64\n",
      "serum_insulin                   768 non-null float64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null float64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>7.680000e+02</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.606642e-13</td>\n",
       "      <td>-1.539856e-15</td>\n",
       "      <td>-6.527692e-15</td>\n",
       "      <td>-9.932633e-14</td>\n",
       "      <td>1.386875e-13</td>\n",
       "      <td>5.152599e-14</td>\n",
       "      <td>-1.510395e-14</td>\n",
       "      <td>2.392017e-13</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>1.000652e+00</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.141852e+00</td>\n",
       "      <td>-2.552931e+00</td>\n",
       "      <td>-4.002619e+00</td>\n",
       "      <td>-2.516429e+00</td>\n",
       "      <td>-1.467353e+00</td>\n",
       "      <td>-2.074783e+00</td>\n",
       "      <td>-1.189553e+00</td>\n",
       "      <td>-1.041549e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.448851e-01</td>\n",
       "      <td>-7.201630e-01</td>\n",
       "      <td>-6.937615e-01</td>\n",
       "      <td>-4.675972e-01</td>\n",
       "      <td>-2.220849e-01</td>\n",
       "      <td>-7.212087e-01</td>\n",
       "      <td>-6.889685e-01</td>\n",
       "      <td>-7.862862e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.509521e-01</td>\n",
       "      <td>-1.530732e-01</td>\n",
       "      <td>-3.198993e-02</td>\n",
       "      <td>-1.230129e-02</td>\n",
       "      <td>-1.815412e-01</td>\n",
       "      <td>-2.258989e-02</td>\n",
       "      <td>-3.001282e-01</td>\n",
       "      <td>-3.608474e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.399473e-01</td>\n",
       "      <td>6.112653e-01</td>\n",
       "      <td>6.297816e-01</td>\n",
       "      <td>3.291706e-01</td>\n",
       "      <td>-1.554775e-01</td>\n",
       "      <td>6.032562e-01</td>\n",
       "      <td>4.662269e-01</td>\n",
       "      <td>6.602056e-01</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.906578e+00</td>\n",
       "      <td>2.542658e+00</td>\n",
       "      <td>4.104082e+00</td>\n",
       "      <td>7.955377e+00</td>\n",
       "      <td>8.170442e+00</td>\n",
       "      <td>5.042397e+00</td>\n",
       "      <td>5.883565e+00</td>\n",
       "      <td>4.063716e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  7.680000e+02                  7.680000e+02    7.680000e+02   \n",
       "mean   2.606642e-13                 -1.539856e-15   -6.527692e-15   \n",
       "std    1.000652e+00                  1.000652e+00    1.000652e+00   \n",
       "min   -1.141852e+00                 -2.552931e+00   -4.002619e+00   \n",
       "25%   -8.448851e-01                 -7.201630e-01   -6.937615e-01   \n",
       "50%   -2.509521e-01                 -1.530732e-01   -3.198993e-02   \n",
       "75%    6.399473e-01                  6.112653e-01    6.297816e-01   \n",
       "max    3.906578e+00                  2.542658e+00    4.104082e+00   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin           BMI  \\\n",
       "count                 7.680000e+02   7.680000e+02  7.680000e+02   \n",
       "mean                 -9.932633e-14   1.386875e-13  5.152599e-14   \n",
       "std                   1.000652e+00   1.000652e+00  1.000652e+00   \n",
       "min                  -2.516429e+00  -1.467353e+00 -2.074783e+00   \n",
       "25%                  -4.675972e-01  -2.220849e-01 -7.212087e-01   \n",
       "50%                  -1.230129e-02  -1.815412e-01 -2.258989e-02   \n",
       "75%                   3.291706e-01  -1.554775e-01  6.032562e-01   \n",
       "max                   7.955377e+00   8.170442e+00  5.042397e+00   \n",
       "\n",
       "       Diabetes_pedigree_function           Age      Target  \n",
       "count                7.680000e+02  7.680000e+02  768.000000  \n",
       "mean                -1.510395e-14  2.392017e-13    0.348958  \n",
       "std                  1.000652e+00  1.000652e+00    0.476951  \n",
       "min                 -1.189553e+00 -1.041549e+00    0.000000  \n",
       "25%                 -6.889685e-01 -7.862862e-01    0.000000  \n",
       "50%                 -3.001282e-01 -3.608474e-01    0.000000  \n",
       "75%                  4.662269e-01  6.602056e-01    1.000000  \n",
       "max                  5.883565e+00  4.063716e+00    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上表知，有许多列的数据与实际不相符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration    425\n",
      "blood_pressure                  419\n",
      "Triceps_skin_fold_thickness     503\n",
      "serum_insulin                   604\n",
      "BMI                             401\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "invalid_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train_data[invalid_col_names] <= 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_data['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(train_data['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250fc51fe48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')#血浆葡萄糖浓度\n",
    "plt.ylabel('Number of occurrences')\n",
    "#由图知此处无效值较多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train_data, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='blood_pressure', data=train_data, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train_data, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.serum_insulin, kde = False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train_data, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.BMI, kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250fcbb7860>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train_data.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\Users\\dell\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train_data.Age, kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250fcad4b70>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train_data.groupby(['Age', 'Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250fd3af630>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train_data.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.329171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.414047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.476909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.808882</td>\n",
       "      <td>4.660524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.681259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>0.109925</td>\n",
       "      <td>1.953325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.766346</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.046014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.622439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.748802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>1.523540</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.807020</td>\n",
       "      <td>0.196681</td>\n",
       "      <td>0.064591</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>0.570172</td>\n",
       "      <td>0.629782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.926869</td>\n",
       "      <td>2.021610</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.213910</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.170442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.191785</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>1.457791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.397653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.347687</td>\n",
       "      <td>1.511083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.960667</td>\n",
       "      <td>2.036530</td>\n",
       "      <td>1.034767</td>\n",
       "      <td>1.942276</td>\n",
       "      <td>0.238963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.012114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.578412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.101523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.312165</td>\n",
       "      <td>0.172520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.142800</td>\n",
       "      <td>1.291553</td>\n",
       "      <td>1.353586</td>\n",
       "      <td>1.092686</td>\n",
       "      <td>0.996229</td>\n",
       "      <td>0.701041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.960667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.428601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>2.444034</td>\n",
       "      <td>1.456996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.069002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.660206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.629782</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.124780</td>\n",
       "      <td>0.701671</td>\n",
       "      <td>1.787882</td>\n",
       "      <td>0.442995</td>\n",
       "      <td>0.061720</td>\n",
       "      <td>0.603256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.511083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>0.109925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.660206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>0.833170</td>\n",
       "      <td>0.298896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.010784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.830381</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.045675</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.718712</td>\n",
       "      <td>0.767420</td>\n",
       "      <td>0.795225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.021610</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.622439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.239392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.404942</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.223895</td>\n",
       "      <td>0.603256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.025338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.745293</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2.124780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.629782</td>\n",
       "      <td>0.898290</td>\n",
       "      <td>0.108056</td>\n",
       "      <td>1.432866</td>\n",
       "      <td>0.945671</td>\n",
       "      <td>1.255820</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>0.603047</td>\n",
       "      <td>1.787882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035628</td>\n",
       "      <td>0.791645</td>\n",
       "      <td>1.000557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2.718712</td>\n",
       "      <td>1.030419</td>\n",
       "      <td>1.291553</td>\n",
       "      <td>0.898290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.185439</td>\n",
       "      <td>2.120497</td>\n",
       "      <td>0.490030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>2.421746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.960667</td>\n",
       "      <td>0.442995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048695</td>\n",
       "      <td>1.085644</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.833170</td>\n",
       "      <td>1.787882</td>\n",
       "      <td>1.353586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.451685</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>1.353586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.015048</td>\n",
       "      <td>1.884928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.148161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.687250</td>\n",
       "      <td>0.574147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.234767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>1.326292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.046014</td>\n",
       "      <td>0.471547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.144658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.464339</td>\n",
       "      <td>1.125938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.952566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.950912</td>\n",
       "      <td>1.291553</td>\n",
       "      <td>1.695058</td>\n",
       "      <td>4.278256</td>\n",
       "      <td>1.578412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>1.063293</td>\n",
       "      <td>0.464339</td>\n",
       "      <td>0.329171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.208549</td>\n",
       "      <td>1.291553</td>\n",
       "      <td>1.125938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.588702</td>\n",
       "      <td>1.767143</td>\n",
       "      <td>0.319855</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>0.936914</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>1.456996</td>\n",
       "      <td>1.353586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.490030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.559592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.596171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.298896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.734247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>2.246785</td>\n",
       "      <td>1.622439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.443156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.787399</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.888288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>1.589290</td>\n",
       "      <td>0.133453</td>\n",
       "      <td>0.215347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.680294</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.830381</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1.530847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1.827813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.298896</td>\n",
       "      <td>2.150354</td>\n",
       "      <td>0.455573</td>\n",
       "      <td>0.064737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.532136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.632365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.342981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.142800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.170732</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0     0.639947                      0.866045             NaN   \n",
       "1          NaN                           NaN             NaN   \n",
       "2     1.233880                      2.016662             NaN   \n",
       "3          NaN                           NaN             NaN   \n",
       "4          NaN                      0.504422             NaN   \n",
       "5     0.342981                           NaN        0.133453   \n",
       "6          NaN                           NaN             NaN   \n",
       "7     1.827813                           NaN             NaN   \n",
       "8          NaN                      2.476909             NaN   \n",
       "9     1.233880                      0.109925        1.953325   \n",
       "10    0.046014                           NaN        1.622439   \n",
       "11    1.827813                      1.523540        0.133453   \n",
       "12    1.827813                      0.570172        0.629782   \n",
       "13         NaN                      2.213910             NaN   \n",
       "14    0.342981                      1.457791             NaN   \n",
       "15    0.936914                           NaN             NaN   \n",
       "16         NaN                           NaN        0.960667   \n",
       "17    0.936914                           NaN        0.133453   \n",
       "18         NaN                           NaN             NaN   \n",
       "19         NaN                           NaN             NaN   \n",
       "20         NaN                      0.142800        1.291553   \n",
       "21    1.233880                           NaN        0.960667   \n",
       "22    0.936914                      2.444034        1.456996   \n",
       "23    1.530847                           NaN        0.629782   \n",
       "24    2.124780                      0.701671        1.787882   \n",
       "25    1.827813                      0.109925             NaN   \n",
       "26    0.936914                      0.833170        0.298896   \n",
       "27         NaN                           NaN             NaN   \n",
       "28    2.718712                      0.767420        0.795225   \n",
       "29    0.342981                           NaN        1.622439   \n",
       "..         ...                           ...             ...   \n",
       "738        NaN                           NaN             NaN   \n",
       "739        NaN                           NaN        0.133453   \n",
       "740   2.124780                           NaN        0.629782   \n",
       "741        NaN                           NaN             NaN   \n",
       "742        NaN                           NaN             NaN   \n",
       "743   1.530847                      0.603047        1.787882   \n",
       "744   2.718712                      1.030419        1.291553   \n",
       "745   2.421746                           NaN        0.960667   \n",
       "746        NaN                      0.833170        1.787882   \n",
       "747        NaN                           NaN        0.133453   \n",
       "748        NaN                      2.148161             NaN   \n",
       "749   0.639947                      1.326292             NaN   \n",
       "750   0.046014                      0.471547             NaN   \n",
       "751        NaN                           NaN        0.464339   \n",
       "752        NaN                           NaN             NaN   \n",
       "753        NaN                      1.950912        1.291553   \n",
       "754   1.233880                      1.063293        0.464339   \n",
       "755        NaN                      0.208549        1.291553   \n",
       "756   0.936914                      0.504422        1.456996   \n",
       "757        NaN                      0.044175             NaN   \n",
       "758        NaN                           NaN        0.298896   \n",
       "759   0.639947                      2.246785        1.622439   \n",
       "760        NaN                           NaN             NaN   \n",
       "761   1.530847                      1.589290        0.133453   \n",
       "762   1.530847                           NaN             NaN   \n",
       "763   1.827813                           NaN        0.298896   \n",
       "764        NaN                      0.011301             NaN   \n",
       "765   0.342981                           NaN             NaN   \n",
       "766        NaN                      0.142800             NaN   \n",
       "767        NaN                           NaN             NaN   \n",
       "\n",
       "     Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                       0.670643            NaN  0.166619   \n",
       "1                            NaN            NaN       NaN   \n",
       "2                            NaN            NaN       NaN   \n",
       "3                            NaN            NaN       NaN   \n",
       "4                       0.670643       0.316566  1.549303   \n",
       "5                            NaN            NaN       NaN   \n",
       "6                       0.329171            NaN       NaN   \n",
       "7                            NaN            NaN  0.414047   \n",
       "8                       1.808882       4.660524       NaN   \n",
       "9                            NaN            NaN       NaN   \n",
       "10                           NaN            NaN  0.748802   \n",
       "11                           NaN            NaN  0.807020   \n",
       "12                           NaN            NaN       NaN   \n",
       "13                           NaN       8.170442       NaN   \n",
       "14                           NaN       0.397653       NaN   \n",
       "15                           NaN            NaN       NaN   \n",
       "16                      2.036530       1.034767  1.942276   \n",
       "17                           NaN            NaN       NaN   \n",
       "18                      1.012114            NaN  1.578412   \n",
       "19                      0.101523            NaN  0.312165   \n",
       "20                      1.353586       1.092686  0.996229   \n",
       "21                           NaN            NaN  0.428601   \n",
       "22                           NaN            NaN  1.069002   \n",
       "23                      0.670643            NaN       NaN   \n",
       "24                      0.442995       0.061720  0.603256   \n",
       "25                           NaN            NaN       NaN   \n",
       "26                           NaN            NaN  1.010784   \n",
       "27                           NaN            NaN       NaN   \n",
       "28                           NaN            NaN       NaN   \n",
       "29                           NaN            NaN  0.239392   \n",
       "..                           ...            ...       ...   \n",
       "738                          NaN       0.223895  0.603256   \n",
       "739                          NaN            NaN  1.025338   \n",
       "740                     0.898290       0.108056  1.432866   \n",
       "741                          NaN            NaN       NaN   \n",
       "742                          NaN            NaN       NaN   \n",
       "743                          NaN            NaN  0.035628   \n",
       "744                     0.898290            NaN  1.185439   \n",
       "745                     0.442995            NaN       NaN   \n",
       "746                     1.353586            NaN  2.451685   \n",
       "747                     1.353586            NaN  2.015048   \n",
       "748                          NaN       0.687250  0.574147   \n",
       "749                          NaN            NaN       NaN   \n",
       "750                          NaN            NaN       NaN   \n",
       "751                     1.125938            NaN  0.952566   \n",
       "752                          NaN            NaN       NaN   \n",
       "753                     1.695058       4.278256  1.578412   \n",
       "754                     0.329171            NaN       NaN   \n",
       "755                     1.125938            NaN  0.588702   \n",
       "756                     1.353586            NaN       NaN   \n",
       "757                          NaN            NaN  0.559592   \n",
       "758                          NaN            NaN  0.734247   \n",
       "759                          NaN            NaN  0.443156   \n",
       "760                          NaN            NaN       NaN   \n",
       "761                     0.215347            NaN  1.680294   \n",
       "762                          NaN            NaN       NaN   \n",
       "763                     2.150354       0.455573  0.064737   \n",
       "764                          NaN            NaN  0.632365   \n",
       "765                          NaN            NaN       NaN   \n",
       "766                          NaN            NaN       NaN   \n",
       "767                     0.215347            NaN       NaN   \n",
       "\n",
       "     Diabetes_pedigree_function       Age  Target  \n",
       "0                      0.468492  1.425995       1  \n",
       "1                           NaN       NaN       0  \n",
       "2                      0.604397       NaN       1  \n",
       "3                           NaN       NaN       0  \n",
       "4                      5.484909       NaN       1  \n",
       "5                           NaN       NaN       0  \n",
       "6                           NaN       NaN       1  \n",
       "7                           NaN       NaN       0  \n",
       "8                           NaN  1.681259       1  \n",
       "9                           NaN  1.766346       1  \n",
       "10                          NaN       NaN       0  \n",
       "11                     0.196681  0.064591       1  \n",
       "12                     2.926869  2.021610       0  \n",
       "13                          NaN  2.191785       1  \n",
       "14                     0.347687  1.511083       1  \n",
       "15                     0.036615       NaN       1  \n",
       "16                     0.238963       NaN       1  \n",
       "17                          NaN       NaN       1  \n",
       "18                          NaN       NaN       0  \n",
       "19                     0.172520       NaN       1  \n",
       "20                     0.701041       NaN       0  \n",
       "21                          NaN  1.425995       0  \n",
       "22                          NaN  0.660206       1  \n",
       "23                          NaN       NaN       1  \n",
       "24                          NaN  1.511083       1  \n",
       "25                          NaN  0.660206       1  \n",
       "26                          NaN  0.830381       1  \n",
       "27                     0.045675       NaN       0  \n",
       "28                          NaN  2.021610       0  \n",
       "29                          NaN  0.404942       0  \n",
       "..                          ...       ...     ...  \n",
       "738                         NaN       NaN       0  \n",
       "739                         NaN  0.745293       1  \n",
       "740                    0.945671  1.255820       1  \n",
       "741                         NaN       NaN       0  \n",
       "742                         NaN       NaN       0  \n",
       "743                    0.791645  1.000557       1  \n",
       "744                    2.120497  0.490030       0  \n",
       "745                    0.048695  1.085644       0  \n",
       "746                         NaN       NaN       1  \n",
       "747                    1.884928       NaN       0  \n",
       "748                         NaN  0.234767       1  \n",
       "749                         NaN  1.425995       1  \n",
       "750                    2.144658       NaN       1  \n",
       "751                         NaN       NaN       0  \n",
       "752                         NaN       NaN       0  \n",
       "753                         NaN       NaN       1  \n",
       "754                         NaN  1.000557       1  \n",
       "755                    1.767143  0.319855       1  \n",
       "756                         NaN  0.490030       0  \n",
       "757                         NaN  1.596171       1  \n",
       "758                         NaN       NaN       0  \n",
       "759                         NaN  2.787399       1  \n",
       "760                    0.888288       NaN       0  \n",
       "761                         NaN  0.830381       1  \n",
       "762                         NaN       NaN       0  \n",
       "763                         NaN  2.532136       0  \n",
       "764                         NaN       NaN       0  \n",
       "765                         NaN       NaN       0  \n",
       "766                         NaN  1.170732       1  \n",
       "767                         NaN       NaN       0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "\n",
    "train_data[train_data < 0] = np.nan  # 对过滤出来的对象进行赋值替换\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       424\n",
      "Plasma_glucose_concentration    425\n",
      "blood_pressure                  419\n",
      "Triceps_skin_fold_thickness     503\n",
      "serum_insulin                   604\n",
      "BMI                             401\n",
      "Diabetes_pedigree_function      473\n",
      "Age                             474\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "medians = train_data.median() \n",
    "train_data = train_data.fillna(medians)\n",
    "\n",
    "print(train_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化\n",
    "y_train = train_data['Target']   \n",
    "X_train = train_data.drop([\"Target\"], axis=1)\n",
    "feat_names = X_train.columns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_part,X_test, y_train_part, y_test =train_test_split(X_train,y_train,test_size=0.3, random_state=0)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "SVC1 = LinearSVC()\n",
    "SVC1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 采用5折交叉验证，用正确率作为评价指标，对线性SVM和RBF核的SVM模型超参数调优。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold: 1, Class dist.: [400 214], Acc: 0.734\n",
      "Fold: 2, Class dist.: [400 214], Acc: 0.747\n",
      "Fold: 3, Class dist.: [400 214], Acc: 0.740\n",
      "Fold: 4, Class dist.: [400 215], Acc: 0.752\n",
      "Fold: 5, Class dist.: [400 215], Acc: 0.739\n",
      "\n",
      "CV accuracy: 0.742 +/- 0.006\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "kfold = StratifiedKFold(n_splits=5,random_state=1).split(X_train, y_train)\n",
    "scores = []\n",
    "for k, (train, test) in enumerate(kfold):\n",
    "     score = SVC1.score(X_train[test], y_train[test])\n",
    "     scores.append(score)\n",
    "     print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1,np.bincount(y_train[train]), score))\n",
    "print('\\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_test, y_test):\n",
    "    # 在训练集上训练SVC\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_test, y_test)\n",
    "    \n",
    "    print(\"C= {} : accuracy= {} \" .format(C, accuracy))\n",
    "    return accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 0.1 : accuracy= 0.7402597402597403 \n",
      "C= 1.0 : accuracy= 0.7402597402597403 \n",
      "C= 10.0 : accuracy= 0.7402597402597403 \n",
      "C= 100.0 : accuracy= 0.7186147186147186 \n"
     ]
    }
   ],
   "source": [
    "C_s = np.logspace(-1, 2, 4)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train_part, y_train_part, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = []\n",
    "for k, (train, test) in enumerate(kfold):\n",
    "    SVC2 =  LinearSVC( C = 1)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    score = SVC1.score(X_train[test], y_train[test])\n",
    "    scores.append(score)\n",
    "    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1,np.bincount(y_train[train]), score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, (train, test) in enumerate(kfold):\n",
    "    SVC3 = LinearSVC(20)\n",
    "    SVC1.fit(X_train,y_train)\n",
    "    score = SVC2.score(X_train[test], y_train[test])\n",
    "    scores.append(score)\n",
    "    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1,np.bincount(y_train[train]), score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_test, y_test):\n",
    "\n",
    "    SVC3 = SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    accuracy = SVC3.score(X_test, y_test)\n",
    "    \n",
    "    print(\"C= {} and gamma = {}: accuracy= {} \" .format(C, gamma, accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 1 and gamma = 0.1: accuracy= 0.7445887445887446 \n",
      "C= 1 and gamma = 3.1622776601683795: accuracy= 0.7186147186147186 \n",
      "C= 1 and gamma = 100.0: accuracy= 0.670995670995671 \n"
     ]
    }
   ],
   "source": [
    "accuracy_s = np.matrix(np.zeros(shape=(5, 3)), float)\n",
    "gamma_s = np.logspace(-1, 2, 3)  \n",
    "oneC = 1\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[1,j] = fit_grid_point_RBF(oneC, gamma, X_train_part, y_train_part, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 10 and gamma = 0.1: accuracy= 0.7402597402597403 \n",
      "C= 10 and gamma = 3.1622776601683795: accuracy= 0.683982683982684 \n",
      "C= 10 and gamma = 100.0: accuracy= 0.6666666666666666 \n"
     ]
    }
   ],
   "source": [
    "oneC = 10\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[2,j] = fit_grid_point_RBF(oneC, gamma, X_train_part, y_train_part, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 100 and gamma = 0.1: accuracy= 0.7445887445887446 \n",
      "C= 100 and gamma = 3.1622776601683795: accuracy= 0.6666666666666666 \n",
      "C= 100 and gamma = 100.0: accuracy= 0.6666666666666666 \n"
     ]
    }
   ],
   "source": [
    "oneC = 100\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[2,j] = fit_grid_point_RBF(oneC, gamma, X_train_part, y_train_part, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 1000 and gamma = 0.1: accuracy= 0.7142857142857143 \n",
      "C= 1000 and gamma = 3.1622776601683795: accuracy= 0.6753246753246753 \n",
      "C= 1000 and gamma = 100.0: accuracy= 0.6666666666666666 \n"
     ]
    }
   ],
   "source": [
    "oneC = 1000\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[2,j] = fit_grid_point_RBF(oneC, gamma, X_train_part, y_train_part, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 500 and gamma = 0.1: accuracy= 0.7142857142857143 \n",
      "C= 500 and gamma = 3.1622776601683795: accuracy= 0.658008658008658 \n",
      "C= 500 and gamma = 100.0: accuracy= 0.6666666666666666 \n"
     ]
    }
   ],
   "source": [
    "oneC = 500\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[2,j] = fit_grid_point_RBF(oneC, gamma, X_train_part, y_train_part, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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